Associated Papers
Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?
Stevens CAT et al.
Journal of the American Heart Association, 2024
38879446 (PubMed )
Supplement: JAH3-13-e034434-s001.pdf
Validation of the 2022 Clinical Diagnostic Criteria of Familial Hypercholesterolemia in Japan.
Tada H et al.
Journal of Atherosclerosis and Thrombosis, 2024
Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.
Ryu J et al.
Medrxiv : the Preprint Server for Health Sciences, 2023
37732177 (PubMed )
Supplement: media-3.zip › Supplementary_Tables › TableS7_tiling_screen.xlsx › TableSX_tiling_screen
Genetic Analysis of Japanese Children Clinically Diagnosed with Familial Hypercholesterolemia.
Nagahara K et al.
Journal of Atherosclerosis and Thrombosis, 2022
Effects of Different Types of Pathogenic Variants on Phenotypes of Familial Hypercholesterolemia.
Tada H et al.
Frontiers in Genetics, 2022
35480308 (PubMed )
Supplement: Presentation1.pdf
Mutation type classification and pathogenicity assignment of sixteen missense variants located in the EGF-precursor homology domain of the LDLR.
Galicia-Garcia U et al.
Scientific Reports, 2020
32015373 (PubMed )
Supplement: 41598_2020_58734_MOESM1_ESM.pdf
Molecular modeling of LDLR aids interpretation of genomic variants.
Klee EW et al.
Journal of Molecular Medicine (berlin, Germany), 2019
30778614 (PubMed )
Supplement: 109_2019_1755_MOESM1_ESM.xlsx › TableS1
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